An efficient job-shop scheduling algorithm based on particle swarm optimization
Expert Systems with Applications: An International Journal
Improved identification of Hammerstein plants using new CPSO and IPSO algorithms
Expert Systems with Applications: An International Journal
Improving fuzzy knowledge integration with particle swarmoptimization
Expert Systems with Applications: An International Journal
Short Communication: An effective TPA-based algorithm for job-shop scheduling problem
Expert Systems with Applications: An International Journal
A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow
Computers and Operations Research
A novel cyclic discrete optimization framework for particle swarm optimization
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Combining PSO and local search to solve scheduling problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A cooperative particle swarm optimizer with statistical variable interdependence learning
Information Sciences: an International Journal
A new hybrid genetic algorithm for job shop scheduling problem
Computers and Operations Research
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence
Hybrid particle swarm optimization and convergence analysis for scheduling problems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Sensor deployment for fault diagnosis using a new discrete optimization algorithm
Applied Soft Computing
Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO)
Applied Soft Computing
Journal of Parallel and Distributed Computing
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The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. In the artificial immune system, the models of vaccination and receptor editing are designed to improve the immune performance. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined by using a set of benchmark instances with various sizes and levels of hardness and is compared with other approaches reported in some existing literature works. The computational results validate the effectiveness of the proposed approach.